Charged system search algorithm for emission constrained economic power dispatch problem

Özyön, Serdar; Temurtaş, Hasan; Durmuş, Burhanettin; Kuvat, Gültekin

doi:10.1016/j.energy.2012.08.008

Cited by 68 publications

(9 citation statements)

References 58 publications

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“…Step 10: Return solution 8.3 | Applications Özyön, Temurtas, Durmus, and Kuvat (2012) used the CSS algorithm for optimization of emission constrained economic power dispatch problem. Kumar and Sahool (2015) applied CSS and magnetic CSS and PSO (Iacca, Caraffini, & Neri, 2014;Wu, Tseng, & Tsai, 2014) algorithm for data clustering problems.…”

Section: Algorithmmentioning

confidence: 99%

Physics‐based search and optimization: Inspirations from nature

Siddique

Adeli

2016

Expert Systems

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Section: Algorithmmentioning

confidence: 99%

Physics‐based search and optimization: Inspirations from nature

Siddique

Adeli

2016

Expert Systems

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“…Also applied to ED problems are metaheuristic algorithms such as Tabu Search Algorithm, 17 Particle Swarm Algorithm, 18 Scatter Search Algorithm, 19 Seeker Optimization Algorithm, 20 and Charged System Search Algorithm. 21 In the literature, multi-objective EELD problem has been solved either directly or converting it into a single-objective problem. Examples are improved Particle Swarm Optimization (PSO), 22 Nondominated Sorting Genetic Algorithm (NSGA), 23 Niched Pareto Genetic Algorithm (NPGA), 24 Strength Pareto Evolutionary Algorithm (SPEA), 25 and Fuzzy-based Bacterial Foraging Algorithms (MBFA).…”

Section: Introductionmentioning

confidence: 99%

Quadratic approximation-based hybrid Artificial Cooperative Search algorithm for economic emission load dispatch problems

Turgut

Demir

2016

Int. Trans. Electr. Energ. Syst.

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Summary In this article, Artificial Cooperative Search (ACS) algorithm is incorporated with the quadratic approximation (QA) operator to solve the multi‐objective economic emission load dispatch (EELD) problems with different generation units. ACS is a Swarm Intelligence–based metaheuristic algorithm, based on the interaction between prey and predator organisms in a habitat, which is effective at global search; however, it does not perform so well at exploring promising regions. The QA operator, on the other hand, is a non‐derivative–based efficient local search method that finds the minimum of a quadratic hyperspace passing through three points in a D‐dimensional space. Solving the EELD problems with the hybridized ACS‐QA algorithm, as being proposed in the present article, leads to more accurate results with fewer function evaluations. Also, multi‐objectivity of the problem is handled by transforming it into a single‐objective problem by using the weighted sum method. The efficiency of the proposed ACS‐QA algorithm is tested in comparison to the algorithms existing in literature by implementing it on six different benchmark optimization problems. Afterward, the proposed ACS‐QA algorithm and the ACS algorithm are implemented on multi‐objective EELD problems with different generation units. The results are compared with the solutions in literature utilizing different metaheuristic optimization algorithms. Both studies firmly showed that the ACS‐QA algorithm is able to find more accurate results even though it uses fewer function evaluation calls.

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“…In this paper, the B-coefficients method is adopted in line with the previous research done in [12,16,26] based on following formula:…”

Section: System and Operational Constraintsmentioning

confidence: 99%

“…A set of Pareto front solutions can be produced by assigning the weight factor k between 0 to 1 [12,14,29]. If k is set to 1, OF represents the minimizing fuel cost only, whereas if k is set to 0, OF is to minimize emission only.…”

Section: Economic-emission Load Dispatch Problemmentioning

confidence: 99%

“…In this approach, the weighted sum method (WSM) is commonly applied to obtain a Pareto optimal solution [10]. The modified bacterial foraging algorithm (MBFA) [11], charged system search algorithm (CSS) [12], genetic algorithm (GA) [13], and gravitational search algorithm (GSA) [14] have been used to solve EELD problems based on this approach. In addition, the conic scalarization method (CSW) was first implemented in [15] for EELD problems and can be an alternative approach to the WSM.…”

Section: Introductionmentioning

confidence: 99%

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Modified particle swarm optimization for economic-emission load dispatch of power system operation

Abdullah

Bakar²,

Rahim³

et al. 2015

Turk J Elec Eng & Comp Sci

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This paper proposes a modified particle swarm optimization considering time-varying acceleration coefficients for the economic-emission load dispatch (EELD) problem. The new adaptive parameter is introduced to update the particle movements through the modification of the velocity equation of the classical particle swarm optimization (PSO) algorithm. The idea is to enhance the performance and robustness of classical PSO. The price penalty factor method is used to transform the multiobjective EELD problem into a single-objective problem. Then the weighted sum method is applied for finding the Pareto front solution. The best compromise solution for this problem is determined based on the fuzzy ranking approach. The IEEE 30-bus system has been used to validate the effectiveness of the proposed algorithm.It was found that the proposed algorithm can provide better results in terms of best fuel cost, best emissions, convergence characteristics, and robustness compared to the reported results using other optimization algorithms.

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Charged system search algorithm for emission constrained economic power dispatch problem

Cited by 68 publications

References 58 publications

Physics‐based search and optimization: Inspirations from nature

Physics‐based search and optimization: Inspirations from nature

Quadratic approximation-based hybrid Artificial Cooperative Search algorithm for economic emission load dispatch problems

Modified particle swarm optimization for economic-emission load dispatch of power system operation

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